The design of controllers for wave energy devices has evolved from early monochromatic impedance-matching methods to complex numerical algorithms that can handle panchromatic seas, constraints, and nonlinearity. However, the potential high performance of such numerical controller comes at a computational cost, with some algorithms struggling to implement in real-time, and issues surround convergence of numerical optimisers. Within the broader area of control engineering, practitioners have always displayed a fondness for simple and intuitive controllers, as evidenced by the continued popularity of the ubiquitous PID controller. Recently, a number of energy-maximising wave energy controllers have been developed based on relatively simple strategies, stemming from the fundamentals behind impedance-matching. This paper documents this set of (5) controllers, which have been developed over the period 2010–2020, and compares and contrasts their characteristics, in terms of energy-maximising performance, the handling of physical constraints, and computational complexity. The comparison is carried out both analytically and numerically, including a detailed case study, when considering a state-of-the-art CorPower-like device.

In order to increase the prevalence of wave energy converters (WECs), they must provide energy at competitive prices, especially when compared with other renewable energy sources. Thus, it is imperative to develop control system technologies that are able to maximize energy extraction from waves, such that the delivered energy cost is reduced. An important part of a model-based controller is the model that it uses. System identification techniques (SITs) provide methodologies to get accurate dynamic models from input-output data. However, even though these techniques are well developed in other application areas, they are seldom used in the context of WECs. This paper proposes several strategies based on SIT to get a linear time-invariant model for a three-body hinge-barge wave energy device using experimental data. The main advantage of the model obtained with this methodology, against other methods such as linear potential theory, is that this model remains valid even for relatively large waves and WEC displacements. Other advantages of this model are its simplicity and the low computational resources that it needs. Numerical simulations are carried out to show the validation of the obtained model against recorded experimental data.